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Bayesian Statistics: The three cultures

(statmodeling.stat.columbia.edu)
309 points luu | 1 comments | 26 Jul 24 17:15 UTC | HN request time: 0s | source
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thegginthesky ◴[26 Jul 24 17:55 UTC] No.41080693[source]▶
I miss the college days where professors would argue endlessly on Bayesian vs Frequentist.

The article is very well succinct and even explains why even my Bayesian professors had different approaches to research and analysis. I never knew about the third camp, Pragmatic Bayes, but definitely is in line with a professor's research that was very through on probability fit and the many iteration to get the prior and joint PDF just right.

Andrew Gelman has a very cool talk "Andrew Gelman - Bayes, statistics, and reproducibility (Rutgers, Foundations of Probability)", which I highly recommend for many Data Scientists

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spootze ◴[26 Jul 24 18:11 UTC] No.41080841[source]▶
Regarding the frequentist vs bayesian debates, my slightly provocative take on these three cultures is

- subjective Bayes is the strawman that frequentist academics like to attack

- objective Bayes is a naive self-image that many Bayesian academics tend to possess

- pragmatic Bayes is the approach taken by practitioners that actually apply statistics to something (or in Gelman’s terms, do science)

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refulgentis ◴[26 Jul 24 18:37 UTC] No.41081070[source]▶
I see, so academics are frequentists (attackers) or objective Bayes (naive), and the people Doing Science are pragmatic (correct).

The article gave me the same vibe, nice, short set of labels for me to apply as a heuristic.

I never really understood this particular war, I'm a simpleton, A in Stats 101, that's it. I guess I need to bone up on Wikipedia to understand what's going on here more.

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thegginthesky ◴[26 Jul 24 19:05 UTC] No.41081312[source]▶
Frequentist and Bayesian are correct if both have scientific rigor in their research and methodology. Both can be wrong if the research is whack or sloppy.
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1. slashdave ◴[26 Jul 24 20:23 UTC] No.41081940[source]▶
I've used both in some papers and report two results (why not?). The golden rule in my mind is to fully describe your process and assumptions, then let the reader decide.